r2 score

README.md

@@ -18,7 +18,7 @@ pinned: false
 - **Live score scraping:** Stays up-to-date with real-time match data.
 - **Intelligent prediction:** Leverages a Random Forest model for comprehensive analysis.
 - **Factors beyond run rate:** Considers wickets left, last 5 overs, and more for precise predictions.
-- **Comprehensive metrics:** Track MSE scores of 11.68 for T20 and 20.44 for ODI formats.
+- **Comprehensive metrics:** Track MSE: 11.68, R2: 80% for T20 and MSE: 20.44, R2: 85% for ODI formats.
 
 ## Get Started
 

model.py

@@ -15,6 +15,7 @@
 import warnings, random
 from sklearn.metrics import mean_absolute_error as mae
 from sklearn.metrics import mean_squared_error as mse
+from sklearn.metrics import r2_score
 import statistics
 
 # from sklearn import tree
@@ -145,6 +146,9 @@ def compare_util(model, fname, x_train, x_test, y_train, y_test, balls_left):
     print(
         f"Model: Train MSE: {mse(h_train_data, y_train_data, squared=False)}, Test MSE: {mse(h_test_data, y_test_data, squared=False)}"
     )
+    print(
+        f"Model: Train R2: {r2_score(y_train_data, h_train_data)}, Test R2: {r2_score(y_test_data, h_test_data)}"
+    )
 
 
 def compare(model, fname, x_train, x_test, y_train, y_test):
@@ -217,6 +221,10 @@ def train(fname, max_depth=-1):
         f"Train MSE: {mse(model.predict(x_train)*x_train['balls_left']/6, y_train*x_train['balls_left']/6, squared=False)}, Test MSE: {mse(model.predict(x_test)*x_test['balls_left']/6, y_test*x_test['balls_left']/6, squared=False)}"
     )
 
+    print(
+        f"Train R2: {r2_score(y_train*x_train['balls_left']/6, model.predict(x_train)*x_train['balls_left']/6)}, Test R2: {r2_score(y_test*x_test['balls_left']/6, model.predict(x_test)*x_test['balls_left']/6)}"
+    )
+
     compare(model, fname, x_train, x_test, y_train, y_test)
 
     evaluate(model, featuresdf, x_test, os.path.basename(fname))